Content

  • INTRODUCTION
  • RESEARCH QUESTIONS
  • DATASET DESCRIPTION
  • RESULTS AND INTERPRETATION
  • KEY FINDINGS AND CONCLUSION
  • REFERENCES

INTRODUCTION AND RESEARCH QUESTIONS

INTRODUCTION

Overview

This report presents key findings from the analysis of chess player ratings for August and September 2025, focusing on player performance, rating changes, and trends.

The study examines how changes in rating points affect overall player outcomes and explores potential differences in ratings between genders. By investigating these aspects, the analysis provides insights into player development, rating dynamics, and trends that may help forecast future performance.

Research Questions

  1. Do chess players’ ratings improve or decline over time, and does this vary by player strength or activity level?

  2. Is there a gender gap in chess rating performance and improvement rates?

  3. Does federation size predict improvement success? Do smaller federations outperform larger ones in rating growth?

DATASET DESCRIPTION

DATASET DESCRIPTION

The dataset consists of two files: fide_ratings_august and fide_ratings_september, obtained from TidyTuesday.
These datasets contain player ratings and additional information about individual chess players.

Summary of August Ratings File :

       id               name               fed                sex           
 Min.   :  100013   Length:201015      Length:201015      Length:201015     
 1st Qu.: 2842263   Class :character   Class :character   Class :character  
 Median :14542960   Mode  :character   Mode  :character   Mode  :character  
 Mean   :22859665                                                           
 3rd Qu.:35829558                                                           
 Max.   :94799962                                                           
    title              wtitle             otitle              foa           
 Length:201015      Length:201015      Length:201015      Length:201015     
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
     rating         games              k              bday     
 Min.   :1400   Min.   : 0.000   Min.   :10.00   Min.   :1900  
 1st Qu.:1564   1st Qu.: 0.000   1st Qu.:20.00   1st Qu.:1973  
 Median :1715   Median : 0.000   Median :40.00   Median :1998  
 Mean   :1745   Mean   : 1.701   Mean   :30.73   Mean   :1991  
 3rd Qu.:1886   3rd Qu.: 0.000   3rd Qu.:40.00   3rd Qu.:2009  
 Max.   :2839   Max.   :45.000   Max.   :40.00   Max.   :2021  

Summary of September Ratings File :

       id               name               fed                sex           
 Min.   :  100013   Length:203191      Length:203191      Length:203191     
 1st Qu.: 2849372   Class :character   Class :character   Class :character  
 Median :14576805   Mode  :character   Mode  :character   Mode  :character  
 Mean   :23022064                                                           
 3rd Qu.:36045730                                                           
 Max.   :94799962                                                           
    title              wtitle             otitle              foa           
 Length:203191      Length:203191      Length:203191      Length:203191     
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
     rating         games              k             bday     
 Min.   :1400   Min.   : 0.000   Min.   :10.0   Min.   :1900  
 1st Qu.:1563   1st Qu.: 0.000   1st Qu.:20.0   1st Qu.:1973  
 Median :1713   Median : 0.000   Median :40.0   Median :1998  
 Mean   :1743   Mean   : 1.619   Mean   :30.8   Mean   :1991  
 3rd Qu.:1884   3rd Qu.: 0.000   3rd Qu.:40.0   3rd Qu.:2009  
 Max.   :2839   Max.   :40.000   Max.   :40.0   Max.   :2021  

Dataset after Merging and Column Transformation :

Integrated Dataset of August and September Ratings
ID Name Federation Sex Birthday August_Rating August_Games September_Rating September_Games Rating_Change Games_Played Age
1503014 Carlsen, Magnus NOR M 1990 2839 0 2839 0 0 0 35
2016192 Nakamura, Hikaru USA M 1987 2807 0 2807 0 0 0 38
2020009 Caruana, Fabiano USA M 1992 2784 0 2789 9 5 9 33
25059530 Praggnanandhaa R IND M 2005 2779 0 2785 9 6 9 20
35009192 Erigaisi Arjun IND M 2003 2776 0 2771 9 -5 9 22

RESULTS AND INTERPRETATION

RATING DISTRIBUTION (RQ1)

GAMES VS. RATING CHANGE (RQ1)

GENDER ANALYSIS: OVERVIEW (RQ2)

Across August and September 2025:

Key Stats: Male: 351,742 | Female: 40,158 | Avg rating gap: 114 points

GENDER GAP ACROSS RATING LEVELS (RQ2)

GENDER & ACTIVITY PATTERNS (RQ2)

FEDERATION CLUSTERS (RQ3)

KEY FINDINGS AND CONCLUSION

KEY FINDINGS AND CONCLUSION

Activity Impact

  • Active players show greater rating volatility
  • Inactive players maintain stable ratings
  • Playing regularly introduces both risk and opportunity

Rating Level Patterns

  • Elite players (2500+) show smallest fluctuations
  • Lower-rated players experience larger swings
  • Rating stability increases with strength

Games vs. Improvement

  • Weak correlation between games and rating gain
  • Quality of opponents matters more than quantity
  • Some players improve significantly with few games

Federations Performance

  • Not all Federations with more player have good average ratings
  • There are some federations with small players but high ratings

REFERENCES

REFERENCES

Fung, Thomas. 2025. “Quarto-Mq-Revealjs-Theme: Macquarie University Quarto Reveal.js Presentation Template.” https://github.com/thomas-fung/quarto-mq-revealjs-theme.
Pedersen, Thomas Lin. 2025. Patchwork: The Composer of Plots. https://CRAN.R-project.org/package=patchwork.
R for Data Science. 2025. “TidyTuesday: FIDE Chess Ratings – Week of 2025-09-23.” https://github.com/rfordatascience/tidytuesday/tree/main/data/2025/2025-09-23.
Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Jeff Allen, and Hadley Wickham. 2025. Plotly: Create Interactive Web Graphics via ’Plotly.js’. https://CRAN.R-project.org/package=plotly.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2025. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.